Real time vs Batch Serving - Learn Data Science with Travis

Real-time-vs-Batch-Serving

Real-time and batch serving are two distinct approaches to data processing and analysis. Real-time serving involves the continuous processing of data as it arrives, enabling immediate insights and actions, which is crucial for applications like fraud detection and live analytics. In contrast, batch serving processes large volumes of data at scheduled intervals, allowing for comprehensive analysis but with a delay in insights. Each method has its advantages and is suited for different use cases, depending on the need for immediacy versus thoroughness in data analysis. Understanding these differences is essential for selecting the right approach for specific applications.

Is Real-Time Processing Worth It For Your Analytical Use Cases?

Towards Data Science

Real-time data pipelines provide a notable advantage over batch processing — data becomes available to consumers faster. In the traditional ETL, you would not be able to analyze events from today…

Batch Processing vs Stream Processing

Level Up Coding

When we encounter systems with large amounts of data, there are 2 main ways we can crunch that data to transform it into something useful for our organisation. One of those approaches is batch…

The magical fusion between batch and streaming insights

Towards Data Science

Business needs data driven insights to reach stakeholders and customers as fast as possible and with clearly defined freshness requirements. The Lambda architecture (Marz, 2015) solves these requireme...

Stateful model serving: how we accelerate inference using ONNX Runtime

Towards Data Science

Stateless model serving is what one usually thinks about when using a machine-learned model in production. For instance, a web application handling live traffic can call out to a model server from…

Batch vs. Online Learning: Which Approach Fits Your Machine Learning Needs? (Part 2)

Python in Plain English

This blog post continues the discussion from my previous blog on batch learning, where I explained how models undergo training on large datasets in one go. In this post, we’ll explore online learning ...

Batch And Streaming Demystified For Unification

Towards Data Science

Understand how batch can be considered a subset of streaming and why data engineering should simplify its usage significantly Continue reading on Towards Data Science

How to Design a Batch Processing?

Towards Data Science

We live in a world where every human interaction becomes an event in the system, whether it’s purchasing clothes online or in-store, scrolling social media, or taking an Uber. Unsurprisingly, all thes...

Using Asyncio and Batch APIs for Remote Services

Mouse Vs Python

Introduction to Batch APIs In modern Python applications, it's common to access remote API using REST or other web-based technologies. Batch APIs are capable of processing multiple requests with a sin...

Four fails and a win at a big data stack for realtime analytics

Towards Data Science

Building a user-friendly app to analyze big data in real time (that is, keeping response times below 60 seconds) is a challenge. In the big data world, you’re either doing batch analytics where…

Do Real-Time Data Pipelines Even Exist?

Towards Data Science

Sharing a fresh perspective on real-time data pipelines Photo by Djim Loic on Unsplash How often have you heard these terminologies — real-time data pipelines or real-time data processing or real-tim...

Introduction to Streaming Frameworks

Towards Data Science

Introduction As data architectures are becoming more and more mature, streaming is no longer considered a luxury but a technology with a wide range of applications across different industries. Because...

Handling Batches

Codecademy

Handling batches is an essential practice in PyTorch for managing and processing large datasets efficiently. PyTorch simplifies batch handling through the DataLoader class. Batch processing groups dat...